Christian Igel, a visitor to Cercia presents a lecture on Gradient-based Optimisation of Support Vector Machines.

Recent studies on gradient-based optimisation of support vector machines (SVMs) are presented. First, efficient second order quadratic programming for large scale SVM learning is discussed. Then model selection for SVMs is considered. In particular, gradient-based maximisation of the kernel-target alignment is proposed to adapt kernel parameters. The method is applied to optimise sequence kernels for the detection of bacterial gene starts.